全球首个，连续时间具身世界模型！任意帧率自由生成
Ranking
Observed public metrics from 1 member.
Merged summary
TL;DR - Tsinghua AIR and UC Berkeley BAIR propose ODEWorld, a continuous-time embodied world model that learns the time-derivative of a latent state ("Physical-Time Flow") instead of frame-to-frame transitions, so future states can be queried at any timestamp via ODE integration. It matters because robot manipulation hinges on moments between camera frames, which discrete next-frame predictors cannot address.
- Static/dynamic decoupling: a frozen DINOv2 encoder plus an initial-state-conditioned dynamic encoder/decoder compress motion into a single 1×768 token, so the velocity field is just a 3-layer MLP with FiLM time conditioning.
- Training supervises the first-order latent time derivative directly, using Jacobian-vector products to project visual-feature change rates into latent space, with Savitzky–Golay derivative filtering to suppress DINOv2 frame-to-frame jitter; reported RankMe effective rank 425.2 vs 376.1 (DINOv2 CLS) and 203.7 (V-JEPA 2), arguing against representation collapse.
- One velocity field yields three capabilities: arbitrary frame-rate generation, temporal in-filling (recovered mid-timesteps after 3× downsampling), and reverse rollouts by negating the integration direction.
- On LIBERO video prediction: 20.53 PSNR / 0.109 LPIPS at 16 frames and 19.46 / 0.134 at 64 frames, beating LDP and V-JEPA 2, at 0.072s latency for 64 frames (~55× faster than LDP, ~8.6× faster than V-JEPA 2).
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全球首个，连续时间具身世界模型！任意帧率自由生成
TL;DR - Tsinghua AIR and UC Berkeley BAIR propose ODEWorld, a continuous-time embodied world model that learns the time-derivative of a latent state ("Physical-Time Flow") instead of frame-to-frame transitions, so future states can be queried at any timestamp via ODE integration. It matters because robot manipulation hinges on moments between camera frames, which discrete next-frame predictors cannot address.
- Static/dynamic decoupling: a frozen DINOv2 encoder plus an initial-state-conditioned dynamic encoder/decoder compress motion into a single 1×768 token, so the velocity field is just a 3-layer MLP with FiLM time conditioning.
- Training supervises the first-order latent time derivative directly, using Jacobian-vector products to project visual-feature change rates into latent space, with Savitzky–Golay derivative filtering to suppress DINOv2 frame-to-frame jitter; reported RankMe effective rank 425.2 vs 376.1 (DINOv2 CLS) and 203.7 (V-JEPA 2), arguing against representation collapse.
- One velocity field yields three capabilities: arbitrary frame-rate generation, temporal in-filling (recovered mid-timesteps after 3× downsampling), and reverse rollouts by negating the integration direction.
- On LIBERO video prediction: 20.53 PSNR / 0.109 LPIPS at 16 frames and 19.46 / 0.134 at 64 frames, beating LDP and V-JEPA 2, at 0.072s latency for 64 frames (~55× faster than LDP, ~8.6× faster than V-JEPA 2).